Advantages and Applications of Transforming Empirical Model Input Space with Dimensional Models

Model-based calibration of steady-state engine operation is commonly performed with highly parameterized empirical models that are accurate but not very robust, particularly when predicting highly nonlinear responses such as diesel smoke emissions. To address this problem, and to boost the accuracy of more robust non-parametric methods to the same level, GT-Power was used to transform the empirical model input space into multiple input spaces that simplified the input-output relationship and improved the accuracy and robustness of smoke predictions made by three commonly used empirical modeling methods: Multivariate Regression, Neural Networks and the k-Nearest Neighbor method. The availability of multiple input spaces allowed the development of two committee techniques: a 'Simple Committee' technique that used averaged predictions from a set of 10 pre-selected input spaces chosen by the training data and the "Minimum Variance Committee" technique where the input spaces for each prediction were chosen on the basis of disagreement between the three modeling methods. This latter technique equalized the performance of the three modeling methods. The successively increasing improvements resulting from the use of a single best transformed input space (Best Combination Technique), Simple Committee Technique and Minimum Variance Committee Technique were verified with hypothesis testing. The transformed input spaces were also shown to improve outlier detection and to improve k-Nearest Neighbor performance when predicting dynamic emissions with steady-state training data. An unexpected finding was that the benefits of input space transformation were unaffected by changes in the hardware or the calibration of the underlying GT-Power model.

P-Technique Factor Analyses of the Multiple Affect Adjective Checklist (MAACL)

The Multiple Affect Adjective Check List (MAACL) has been found to have five first-order factors representing Anxiety, Depression, Hostility, Positive Affect, and Sensation Seeking and two second-order factors representing Positive Affect and Sensation Seeking (PASS) and Dysphoria. The present study examines whether these first- and second-order conceptions of affect (based on R-technique factor analysis) can also account for patterns of intraindividual variability in affect (based on P-technique factor analysis) in eight elderly women. Although the hypothesized five-factor model of affect was not testable in all of the present P-technique datasets, the results were consistent with this interindividual model of affect. Moreover, evidence of second-order (PASS and Dysphoria) and third-order (generalized distress) factors was found in one data set. Sufficient convergence in findings between the present P-technique research and prior R-technique research suggests that the MAACL is robust in describing both inter- and intraindividual components of affect in elderly women.

A Measurement Model of Cortisol Reactivity of Healthy Older Adults

Research on the physiological adaptation process has found that stress is associated with the rate of cortisol secretion, the main hormone that reflects stress. However, considerable variation among subjects has been reported. Using a sample of older adults (N=46), we tested the hypothesis that cortisol reactivity is composed of (1) a situation-related component representing hypothalamic influence on cortisol secretion observed on three different occasions, and (2) a stable component representing a general trait responsible for cortisol responses observed from occasion to occasion. LISREL VIII was used to test this hypothesis. Results indicated that a homogeneous reliability model was not supported by the data. A congeneric measurement model represented a better fit to the data. Results suggest that subjects have consistent patterns of response during separate experimental occasions. However, results do not suggest a consistent pattern of response over time. The main implication of these results is that salivary cortisol measures are sensitive to experimental stress situations. As such, this noninvasive method may be useful in examining adaptive responses to stress.